Ariel Fuxman
ABSTRACT
In the sponsored search model, search engines are paid by businesses that are interested in displaying ads for their site alongside the search results. Businesses bid for keywords, and their ad is displayed when the keyword is queried to the search engine. An important problem in this process is 'keyword generation': given a business that is interested in launching a campaign, suggest keywords that are related to that campaign. We address this problem by making use of the query logs of the search engine. We identify queries related to a campaign by exploiting the associations between queries and URLs as they are captured by the user's clicks. These queries form good keyword suggestions since they capture the "wisdom of the crowd" as to what is related to a site. We formulate the problem as a semi-supervised learning problem, and propose algorithms within the Markov Random Field model. We perform experiments with real query logs, and we demonstrate that our algorithms scale to large query logs and produce meaningful results.
- V. Abhishek and K Hosanagar. Keyword generation for search engine advertising using semantic similarity between terms. International Conference on Electronic Commerce, pages 89--94, 2007. Google Scholar
Digital Library
- R. Andersen and K. Lang. Communities from seed sets. WWW, pages 223--232, 2006. Google ScholarDigital Library
- R. Baeza-Yates and A. Tiberi. Extracting semantic relations from query logs. KDD, pages 76--85, 2007. Google ScholarDigital Library
- K. Bartz, V. Murthi, and S. Sebastian. Logistic regression and collaborative filtering for sponsored search term recommendation. Second Workshop on Sponsored Search Auctions, 2006.Google Scholar
- D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. KDD, pages 407--416, 2000. Google ScholarDigital Library
- A. Broder, M. Fontoura, E. Gabrilovich, A. Joshi, V. Josifovski, and T. Zhang. Robust classification of rare queries using web knowledge. SIGIR, pages 231--238, 2007. Google ScholarDigital Library
- Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien. Semi-Supervised Learning. The MIT Press, 2006. Google ScholarDigital Library
- N. Craswell and M. Szummer. Random walks on the click graph. SIGIR, pages 239--246, 2007. Google ScholarDigital Library
- P. Doyle and L. Snell. Random Walks and Electrical Networks. Mathematical Association of America, 1984.Google Scholar
- E. Frank, G. Paynter, I. Witten, C. Gutwin, and C. Nevill-Manning. Domain-specific keyphrase detection. Proc. of IJCAI, pages 668--673, 1999. Google ScholarDigital Library
- Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. VLDB, pages 576--587, 2004. Google ScholarDigital Library
- A. Hulth. Improved automatic keyword extraction given more linguistic knowledge. EMNLP, pages 216--223, 2003. Google ScholarDigital Library
- M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. In M. I. Jordan, editor, Learning in Graphical Models. Kluwer Academic Publishers, Norwell MA., 1998. Google ScholarDigital Library
- A. Joshi and R. Motwani. Keyword generation for search engine advertising. ICDM Workshops, pages 490--496, 2006. Google ScholarDigital Library
- D. Kelleher and S. Luz. Automatic hypertext keyphrase extraction. IJCAI, pages 1608--1609, 2005. Google ScholarDigital Library
- J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999. Google ScholarDigital Library
- S. Li. Markov random field modeling in computer vision. Springer-Verlag, 1995. Google ScholarDigital Library
- J. Rocchio. The SMART Retrieval System: Experiments in Automatic Document Processing, chapter Relevance feedback in Information Retrieval, pages 313--323. Prentice Hall, 1971.Google Scholar
- D. Shen, R. Pan, J. Sun, J. Pan, K. Wu, J. Yin, and Q. Yang. Q2C@UST: Our winning solution to query classification in KDDCUP 2005. SIGKDD Explorations, 7:100--110, 2005. Google ScholarDigital Library
- P. Turney. Learning algorithms for keyphrase extraction. Information Retrieval, 2(4):303--336, 2000. Google ScholarDigital Library
- P. Turney. Coherent keyphrase extraction via web mining. IJCAI, pages 434--439, 2003. Google ScholarDigital Library
- J. Wen, J. Nie, and H. Zhang. Clustering user queries of a search engine. WWW, pages 162--168, 2001. Google ScholarDigital Library
- G. Xue, Y. Yu, D. Shen, Q. Yang, H. Zeng, and Z. Chen. Reinforcing web-object categorization through interrelationships. Data Mining and Knowledge Discovery, 12:229--248, 2006. Google ScholarDigital Library
- G. Xue, H. Zeng, Z. Chen, Y. Yu, W. Ma, W. Xi, and W. Fan. Optimizing web search using web click-through data. CIKM, pages 118--126, 2004. Google ScholarDigital Library
- Y. Yang. An evaluation of statistical approaches to text categorization. Information Retrieval, 1(1-2):69--90, 1999. Google ScholarDigital Library
- W. Yih, J. Goodman, and V. Carvalho. Finding advertising keywords on web pages. WWW, pages 213 -- 222, 2006. Google ScholarDigital Library
- X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. ICML, pages 912--919, 2003.Google ScholarDigital Library
Index Terms
- Using the wisdom of the crowds for keyword generation
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